
We also carried out pixel variation analysis on the synthesised images by using the second order features of the synthesized images, which are based on the gray level co-occurrence matrix (GLCM) [1]}. We used the aforementioned technique to determine the Entropy, Homogeneity and Correlation of the synthesised images in comparison with state-of-the-art methods as shown in Figure 4. Entropy is useful for assessing sharpness while Homogeneity and Correlation are useful for evaluating the Contrast of an image. Entropy and Correlation increase in image quality, whereas Homogeneity energy values decrease with increase in image quality. From the Entropy plot in 5(a), our model performs decently well by over 3.5% compared to the QAGAN and WAGAN. The Contrast level improves drastically for our approach as compared to the other methods that are closely matched at a tolerance of about 2%. We also observed that most models possess similar homogeneity values except our model and QAGAN which reflect significant performance values.
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